#' @title Density Penalized Learner
#' @author RaphaelS1
#' @name mlr_learners_dens.pen
#'
#' @description
#' Density estimation using penalized B-splines with automatic selection of smoothing parameter.
#' Calls [pendensity::pendensity()] from \CRANpkg{pendensity}.
#'
#' @template learner
#' @templateVar id dens.pen
#'
#' @references
#' `r format_bib("schellhase2012density")`
#'
#' @template seealso_learner
#' @template example
#' @export
LearnerDensPenalized = R6Class("LearnerDensPenalized",
  inherit = mlr3proba::LearnerDens,

  public = list(
    #' @description
    #' Creates a new instance of this [R6][R6::R6Class] class.
    initialize = function() {
      ps = ps(
        base = p_fct(default = "bspline",
          levels = c("bspline", "gaussian"), tags = "train"),
        no.base = p_dbl(default = 41, tags = "train"),
        max.iter = p_dbl(default = 20, tags = "train"),
        lambda0 = p_dbl(default = 500, tags = "train"),
        q = p_dbl(default = 3, tags = "train"),
        sort = p_lgl(default = TRUE, tags = "train"),
        with.border = p_uty(tags = "train"),
        m = p_dbl(default = 3, tags = "train"),
        eps = p_dbl(default = 0.01, tags = "train")
      )

      super$initialize(
        id = "dens.pen",
        packages = c("mlr3extralearners", "pendensity"),
        feature_types = c("integer", "numeric"),
        predict_types = c("pdf", "cdf"),
        param_set = ps,
        man = "mlr3extralearners::mlr_learners_dens.pen",
        label = "Penalized Density Estimation"
      )
    }
  ),

  private = list(
    .train = function(task) {

      pars = self$param_set$get_values(tags = "train")
      fit = invoke(pendensity::pendensity,
        form = task$data()[[1]] ~ 1,
        .args = pars)

      pdf = function(x) {} # nolint
      body(pdf) = substitute({
        invoke(pendensity::dpendensity,
          x = fit,
          val = x)
      })

      cdf = function(x) {} # nolint
      body(cdf) = substitute({
        invoke(pendensity::ppendensity,
          x = fit,
          val = x)
      })

      base = if (is.null(pars$base)) {
        "gaussian"
      } else {
        pars$base
      }
      distr6::Distribution$new(
        name = paste("Penalized Density", base),
        short_name = paste0("PenDens_", base),
        pdf = pdf, cdf = cdf, type = set6::Reals$new())
    },

    .predict = function(task) {
      newdata = task$data()[[1]]
      pars = self$param_set$get_values(tags = "predict")
      invoke(list, pdf = self$model$pdf(newdata), cdf = self$model$pdf(newdata), .args = pars)
    }
  )
)

.extralrns_dict$add("dens.pen", LearnerDensPenalized)
